The present invention relates to cellular telephone systems and, in particular, to a method and apparatus for controlling and optimizing the utilization of cellar telephone systems.
A cellular telephone system normally services an area populated by a number of users. Looking at FIG. 1, the service area of the system typically is divided up into cells (C1-C10), each equipped with a base station (BS1-BS10) capable of communicating with a number of users (M1-10) within the cell. Inherently, each cell has a maximum number of users that can be handled simultaneously. This is referred to as the cell's capacity. In addition, the service area containing the cells has an overall capacity. A critical factor which is taken into account in providing cellular telephone service to subscribers is the overall capacity of the system.
A basic problem with designing any cellular system is how much overall and individual cell capacity should be provided to ensure adequate service while maximizing revenues generated from the users of the cellular system. Complicating the design of such a system is the fact that the number of calls, or demand for use of the system, varies dramatically in each cell according to the time of day. On one hand, insufficient capacity during peak hours of system operation will cause congestion, and the inability to provide adequate service to users. On the other hand, providing too much unused capacity during off-hours under utilizes resources that increases overall cost of system operation. Therefore, it is important to optimize the trade-off between adequate capacity to handle anticipated subscriber demand for the system during peak usage while minimizing the unused capacity during off-hours. The more difficult of these two is how to maximize system usage during the hours when the system's capacity is largely idle.
Various methods and systems have been devised in order to maximize usage of the overall capacity of a cellular system at every time of day. In the past this has been attempted by varying the rate charged to subscribers of the system and its services at different times. For example, during peak hours, such as business hours when demand is at its greatest, all subscribers pay a full rate. However, as subscriber demand decreases, for example, during the evening and weekend hours, rates are reduced in one or more steps in order to encourage increased utilization of the system.
Varying the price is an effective means for regulating system demand because of the price sensitivity of subscribers. Certain subscribers are more price sensitive than others. This is known as a subscriber's price elasticity. For example, a personal use subscriber's price elasticity is much greater than a business use subscriber because, unlike a business use subscriber, a personal use subscriber's need to make a call is usually much less urgent or necessary. As a result, personal use subscribers may delay or make fewer calls for no other reason than the price of the call or because it comes out of their own pocket. Accordingly, personal use subscribers are more likely to be induced to use a system when rates are lower.
Cellular service providers often charge higher rates during the week, especially during working hours, for cellular calls. Because of these higher rates, certain subscribers who would otherwise use the cellular phone system are discouraged from making calls due to the higher rates. During peak hours, when the system is being used to near capacity, this is not a problem for the service provider because maximum revenue is being generated from system usage. However, when the system is not being used to near capacity, the service provider is not deriving any revenue from the unused system capacity and thus would like to be able to attract additional subscribers to make use of the system.
Further complicating the problem of maximizing use of available system capacity is that, even during peak hours of the overall system, certain cells may have surplus capacity and, therefore, are not generating their potential maximum revenue. For example, in FIG. 1 a cell in a city center (C1) may have its peak hour in the middle of the day, while a cell in a suburb (C9) may have its peak hour later in the afternoon. FIGS. 2A and 2B illustrate this problem in another way. FIG. 2A is an example of a macro-cell 21 with one micro-cell 22 indicated within. As illustrated in FIG. 2B while the load approaches maximum capacity for the macro-cell 21, a micro-cell 22 may only be at 60% capacity and thus have an unused surplus capacity even though the macro-cell 21 is congested. Typically, with state of the art cellular systems, service providers are forced to take all cells into account when choosing the time of day for rate reduction. Because the macro-cell 21 is at near capacity, the service provider cannot reduce the rate to increase revenues from the micro-cell 22. Additionally complicating matters is that by instantly lowering the rate the load on the entire system might sharply increase. In other words, these systems fail to take into account that different types of subscribers display different price elasticities and thus fail to maximize revenue for the system providers. These systems also do not address the problem that cheaper rates can only be offered when there is a substantial system wide excess capacity, because the load on the system might sharply increase with any broadcast of the lowering of the rate. Thus, if there is insufficient excess capacity, and a lower rate is offered, congestion will likely result.
Various systems have been contemplated in an attempt to address some of these problems. According to one approach described in U.S. Pat. No. 5,303,297 to Hillis, it has been suggested that system demand be monitored, and based on demand, a charge rate is dynamically calculated and broadcast to subscribers to encourage subscribers to use the system during non-peak times. However, Hillis does not describe how this charge rate is calculated. While the broadcast rate message solution does provide more flexibility than previous systems, it is not a complete solution to maximizing overall use of a system's excess capacity. The system of Hillis fails to consider a particular subscriber's price elasticity and thus the system indiscriminantly discourages some subscribers use of the system. This further results in inefficient use of the system's excess capacity and loss of potential revenue.
It has also been suggested that mobile phone calls could be charged according to where the calls are made, for instance, one price in an office, another on the street, and a third at home, etc. However, in this solution, as with the one described above, overload which might result from a decreased rate can only be alleviated by indiscriminately discouraging certain subscribers service and therefore cannot provide overall optimization of generating maximum revenues from the system.